dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
Authors: Hubert Baniecki, Wojciech Kretowicz, Piotr Piątyszek, Jakub Wiśniewski, Przemysław Biecek
JMLR 2021 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To facilitate the responsible development of machine learning models, we introduce dalex, a Python package which implements a model-agnostic interface for interactive explainability and fairness. It adopts the design crafted through the development of various tools for explainable machine learning; thus, it aims at the unification of existing solutions. This library s source code and documentation are available under open license at https://python.drwhy.ai. |
| Researcher Affiliation | Collaboration | 1Faculty of Mathematics and Information Science, Warsaw University of Technology, Poland 2Samsung Research & Development Institute, Poland |
| Pseudocode | No | The paper describes the architecture and features of the dalex Python package, including illustrative code snippets for its usage, but it does not contain any structured pseudocode blocks or algorithms describing a specific method implemented or developed within the paper. |
| Open Source Code | Yes | This library s source code and documentation are available under open license at https://python.drwhy.ai. |
| Open Datasets | No | The paper introduces a software package and its functionalities but does not conduct experiments on specific datasets or provide access information for any open datasets used in its own research. |
| Dataset Splits | No | The paper does not describe any experiments that would require dataset splits, as its focus is on introducing a software package rather than reporting empirical results on specific datasets. |
| Hardware Specification | No | The paper describes a software package and its capabilities but does not mention any specific hardware (like GPU or CPU models) used for experiments. |
| Software Dependencies | No | The paper mentions various Python libraries that dalex interacts with or builds upon, such as 'scikit-learn', 'tensorflow', 'xgboost', 'numpy', 'pandas', and 'plotly', and states 'version 1.3 for Python 3.9' for dalex itself. However, it does not provide specific version numbers for these external software dependencies, which are required for full reproducibility. |
| Experiment Setup | No | The paper introduces a software package and its architecture; it does not describe any specific experiments, hyperparameters, or training configurations. |